Omega 29 (2001) 113–124

www.elsevier.com/locate/dsw

The relationship between JIT practices and type of production system Richard E. Whitea , Victor Prybutokb; ∗ a University

b BCIS

of North Texas, College of Business Administration, P.O. Box 305429, Denton, TX 76203-5429, USA Department, University of North Texas, College of Business Administration, Denton, TX 76203-4935, USA Received 27 August 1999; accepted 23 June 2000

Abstract After World War II, the Japanese incrementally applied new management practices to improve their global competitiveness. With re nement and systematic integration of these new practices the Japanese achieved a new manufacturing paradigm and, by the 1970s, a competitive superiority in the marketplace. In an eort to emulate the success achieved by Japanese manufacturers, US managers began to apply these new management practices in their organizations. These management practices were introduced as just-in-time (JIT) manufacturing. US managers have progressed through a series of trial and error eorts to apply these new management practices and still do not understand many of the issues associated with JIT implementations. This study attempts to address some of the misunderstandings associated with JIT implementations. A systems approach is utilized for collecting data and analyzing pertinent relationships associated with JIT implementations in US manufacturers. Findings from the study suggest that an association exists between implemented JIT practices and type of production system. In addition, this is the rst study to show the bene ts attributed to JIT implementation as a function of implementation status of speci c JIT management practices and type of production system. ? 2001 Elsevier Science Ltd. All rights reserved. Keywords: JIT; Japanese management practices; Production processes; Lean manufacturing; US manufacturers

1. Introduction Japanese management practices have been the focus of US managers’ eorts to adopt dierent approaches that provide the same or higher output levels for their organizations with fewer resources for more than a decade. These Japanese management practices, introduced to US manufacturers as just-in-time (JIT) manufacturing, were presented using a variety of descriptions in the early literature [1– 4]. Later descriptions of this organizational phenomenon [5] generally involve broad-based production systems that consist of various management practices associated with ecient material

ow, improved quality, and increased employee involvement systems [6 –10]. However, the names used for these

∗ Corresponding author. Tel.: +1-940-565-3036; fax: +1-940565-4930. E-mail address: [email protected] (V. Prybutok).

management practices were not consistent, i.e., just-in-time manufacturing, total enterprise manufacturing, world class manufacturing, and lean production [6,10 –12]. In reference to the names used for JIT, Hall [7] states “None of the names often used for this philosophy suggest its total power and scope : : : and none are universally used” (p. 23). JIT is a misused term that is less than adequate to describe this broad production system, but it is still the best term available because it is a more universally accepted term than any of the alternatives. Therefore, JIT is used throughout this paper to describe a broad-based production system that strives to achieve excellence. Unfortunately, some confusion about JIT still exists and unanswered questions remain about implementation issues associated with JIT systems [13–15]. In this study we investigate JIT implementations in US manufacturers. JIT manufacturing is de ned as a system composed of various management practices. Next, the methodology employed for investigating JIT implementations is presented. We assess the individual practices

0305-0483/01/$ - see front matter ? 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 5 - 0 4 8 3 ( 0 0 ) 0 0 0 3 3 - 5

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associated with JIT, then by aggregating the knowledge gained about the practices (system components) and their interrelationships, a comprehensive understanding of JIT implementations is achieved. The relationships are analyzed and the results of the study are discussed. Finally, the ndings of the study are summarized and the study’s contribution to understanding JIT implementations in US manufacturers is discussed. 2. Just-in-time manufacturing The overall objective of a JIT system is to continuously improve the organization’s productivity, quality, and exibility [6,7,10,11]. Each element of a JIT system provides some bene t for a manufacturer, but the application of each element potentially involves only certain areas in the organization, and unless a systems perspective is employed, the areas optimize locally, rather than at the organization level [8]. Consequently, the potential synergic bene ts are not fully realized until all elements of a JIT system are integrated [18,19]. In a related survey study, White et al. [16] collected data from large and small manufacturers to investigate JIT implementations. The instrument used to collect the data measured a set of 10 JIT management practices (representing a holistic understanding of JIT systems) and associated implementation variables. The 10 JIT management practices examined in the study include the following: focused factory, reduced setup times, group technology, total preventive maintenance, multifunction employees, uniform workloads, Kanban, total quality control, quality circles, and JIT purchasing. In a separate survey study, a dierent survey instrument centered around the 10 JIT management practices identi ed by White et al. [16] was used to collect data for modeling JIT systems [17]. Ten subscales were developed (one for each of the 10 JIT practices) in the instrument and subsequently, data were collected from three professional organizations. Upon modeling the data, Davy et al. [17] suggested that the results represented the systems perspective and integrative thinking associated with JIT. Systematic integration of the 10 JIT management practices (presented previously) represents holistic JIT systems as presented throughout this paper. 3. JIT implementations and US manufacturers 3.1. JIT implementations Implementations of JIT in US manufacturers often involve adopting just a few of the management practices associated with JIT [18–21]. As a result of this selective process the frequencies of JIT practices implemented by US manufacturers often dier among the various JIT practices [22–25].

In addition, researchers suggest the practices implemented are typically the ones easiest to implement, but not necessarily the ones that provide the greatest bene ts [22,23]. The piece-meal approach to adopting JIT used by US manufacturers occurs despite research ndings that suggest the synergic bene ts desired by US manufacturers cannot be fully realized until all JIT practices are integrated into a holistic management system [5,8,18,19]. Goyal and Deshmukh [18] state that most of the JIT literatures con rms that understanding the concept of JIT requires a systems perspective. Bene ts attributed to implementing JIT typically include reduced throughput time [22,23,26], improved internal quality [27,28], improved external quality [27,29,30], improved labor productivity [22,29,31], reduced inventory levels [32–34], and lower unit cost [35 –37]. Moreover, the ndings of JIT research generally suggest the longer the JIT system is in place the greater the bene ts achieved [38], and the greater the extent of JIT implementation the greater the success achieved [35,39 – 41]. 3.2. US production systems The framework for understanding JIT implementations in US manufacturers draws from Thompson’s [42] concept of traditional US organizations and Hayes and Wheelwright’s [43] continuum of production processes. Thompson [42] posits that US manufacturers have traditionally used buers or inventories to reduce the eects of uncertainties on the organization’s internal core (technological activities). Buers between the internal core and the external core (input and output activities) allow for developing greater eciencies among the activities within the internal core; this is achieved by increasing the level of interdependence across the activities in the internal core. A reclassi cation of the ends of Hayes and Wheelwright’s [43] continuum of production processes (project=job shop and assembly line=continuous ow) provides a clearer distinction of processes and associated characteristics that support Thompson’s [42] concept. For example, with movement from one end of Hayes and Wheelwright’s [43] continuum (project=job shop) to the other end (assembly line=continuous ow) increasingly higher levels of raw materials and nished goods exist to protect the internal core and increasing lower levels of work-in-process inventories exist among the activities of the internal core. At the project=job shop end, high levels of work-in-process inventories exist to buer among the technological activities and lower levels of inventories exist to buer the internal core from the input and output activities (see Fig. 1). Batch, the production process that falls in the middle, in a sense, is a hybrid of the revised processes on the ends of the continuum. Since batch does not provide a clear distinction for dierentiating from either of the ends, it is not included as a classi cation of production processes in this study. Traditional nonrepetitive production systems (project=job shop) are capable of producing a high variety of products; however, the high levels of WIP inventories (see Fig. 1)

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Fig. 1. Traditional inventory buers in US manufacturers.

associated with nonrepetitive production suggest that ineciency exists among the technological activities [42]. In contrast to nonrepetitive production systems, repetitive production systems (assembly line=continuous ow) are typically ecient, but lack the exibility to produce a high variety of products. In repetitive production systems, inventories are used as buers between input activities and the technological activities, and the technological activities and output activities; this allows for developing eciency among the technological activities [42]. Other characteristics of the revised extremes of Hayes and Wheelwright’s [43] continuum are presented in Table 1. As with Thompson’s [42] concept, Arogyaswamy and Simmons [44] suggest that the key in implementing JIT is to increase the intensity of interdependence between operations while emphasizing global rather than local optimization. According to Arogyaswamy and Simmons the rst step in the implementation process is to make the interdependence between operations obvious. This should be accomplished by introducing the pull system in the following sequence of JIT practices: cellular layout (an aspect of group technology), reduced setup times, Kanban and uniform workload. Since the levels of buers vary between nonrepetitive and repetitive processes [43], the assumption is that the value of implementing speci c JIT management practices for in-

creasing interdependencies may dier dependent upon whether the process is nonrepetitive or repetitive. 3.3. Association between JIT implementations and US production systems Shingo [1] suggested that the Toyota Production System (JIT) is applicable to all factories, but warned that the system should be adapted to the characteristics of each particular plant. Research in this area provides some evidence that the JIT practices implemented is in uenced by type of production system [1,25,38,45]. Each type of production system may have characteristics that are the same or similar to characteristics conducive for implementing certain JIT management practices. However, when JIT practices deviate substantially from existing production system characteristics, opportunities for improvement may be greater because of the substantial change that occurs with implementation. For example, with the high worker skill level and large worker job content typically associated with nonrepetitive production systems, it would seem easier to implement the JIT practice of multifunction employees in nonrepetitive than in repetitive production systems. Because the employees in nonrepetitive production systems are generally of higher skill level

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Table 1 Characteristics of production processesa Characteristics

Production processes Nonrepetitive

Repetitive

Variety of products Material requirements Scheduling Production runs Setups Type of equipment utilized Worker job content (scope) Worker skill level Control over suppliers Inventory levels Raw WIP Finished goods

Customized Dicult to predict Uncertain, frequent changes Short Dierent every job General purpose Large High Low

Standardized Very predictable Fixed schedule, in exible Long Very few and costly Special purpose Small Low High

Low High Low

High Low High

a Source:

Adapted from Hayes and Wheelwright [43].

initially, the bene t for the system may not be as great as that in a repetitive production system. Based on the evidence presented, supported with the characteristics of production systems derived from Hayes and Wheelwright [43], the reasoning leads to the rst hypothesis (null form): Hypothesis H0 1: No association exists between the JIT practices implemented and type of production system. 3.4. JIT practices and associated beneÿts in US production systems JIT researchers suggest that certain production systems allow for easier and lower cost implementation of some JIT management practices compared to other JIT practices [22,32,46]; accordingly, certain production systems oer greater opportunities for improvement from implementing some JIT practices compared to other JIT practices [22,27,28,46]. The second hypothesis (null form) is based on these ndings and the characteristics of production systems derived from Hayes and Wheelwright’s [43] work. Hypothesis H0 2: The relative bene t from each JIT practice is the same regardless of type of production system. Though numerous other variables may in uence the relationships investigated in this study, this work is focused on the association between JIT systems and type of production system. Consistent with the focus of this work the relationship between JIT and other variables was not explored. 4. Methodology This research involves a cross-sectional eld study using survey methodology in dierent manufacturing environ-

ments across a variety of US manufacturing organizations. Unfortunately, most organizations are prone to publicize and perhaps exaggerate examples of successful implementation while downplaying or hiding instances of failure. Therefore, to avoid bias about the success of implementing speci c JIT management practices we surveyed well-informed middleand upper-level managers with hands-on experience with JIT manufacturing. These respondents were sought because of their broad perspective of the organization’s activities and because of their knowledge of associated implementation issues. 4.1. Target population The data analyzed in this study were collected from members of the Association for Manufacturing Excellence (AME). AME members represent all types of production processes and all functions within those operations. In this study we investigate JIT implementations in US manufacturers. The methodology for this work overlaps the one used by White et al. [16] because the data analyzed for this study used the same respondents. However, the data in this study constituted a distinct and dierent data set because the variables selected were dierent. 4.2. Questionnaire The Total Design Method [47] for writing questions and constructing the questionnaire was followed for developing the survey instrument. The items in the questionnaire were structured as close-ended questions with ordered choices. The structure of the questions used to collect information on each of the key variables assessed in this study included production systems, implementation status of JIT practices, and bene ts associated with JIT implementations.

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Production systems. Items used to collect information for type of production system consisted of percentage measures of annual sales produced by type of production process. De nitions of types of production processes based on Hayes and Wheelwright’s [43] classi cation of manufacturing processes (project, job shop, batch, assembly line, and continuous ow) were included in the questionnaire. Implementation status of JIT practices. Each of the 10 JIT practices that represent the holistic understanding of JIT systems formed an item used to collect information on implementation status of JIT practices. De nitions for each of these JIT management practices were used in data collection to reduce any misunderstandings that may exist (see [16] for these de nitions). The data for the practices were scored based on the midpoint of the assessment scale: not implemented (scored “0”), implementation started within the last year (scored “0.5”), implementation started 1–3 years ago (scored “2”), implementation started 3–5 years ago (scored “4”), and implementation started more than 5 years ago (scored “6”). Beneÿts associated with JIT implementations. Data were collected on six bene ts associated with JIT systems: throughput time (lead time — includes time from order release to job completion), internal quality (defects, rework, etc.), external quality (warranties, returns, etc.), labor productivity, inventory levels, and unit cost. The data on four on these bene ts (throughput time, internal quality, external quality, and labor productivity) were collected as “better” or “not better”. The data of the other two bene ts (inventory levels and unit cost) were collected as “lower” or “not lower”. Validation. Focus groups consisting of experts in the areas, a pretest, and a pilot study were used to clarify items on the questionnaire and further develop the comprehensiveness of the instrument (for further details see [16]). In addition, follow-up interviews with the respondents from the pretest allowed for additional clari cation of ambiguous items. Follow-up interviews of the respondents allowed for feedback which was reviewed and necessary revisions were made to the questionnaire prior to data collection. 4.3. Data collection, review and reclassiÿcation The data collection process consisted of two mailings. Approximately 5 weeks after the initial mailing a follow-up mailing was performed. Of the 2640 surveys initially mailed a total of 1165 surveys were completed and returned for an overall response rate of 44.1%. A review of the data allowed for identi cation of data omitted from the nal sample for the following: completed surveys that were from multiple respondents of the same organization, academicians and=or consultants (N = 70); completed surveys from respondents’ whose organizations had not implemented any of the JIT practices (N = 34); and

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completed surveys that had incomplete data pertaining to any of the key variables assessed in this study (N = 144). Since the focus of the study was on manufacturers, 76 cases were omitted because less than 75% of the rm’s sales were generated from manufacturing. The data for project and job shop production processes were combined to form a revised classi cation representing nonrepetitive production systems. Assembly-line and continuous ow data were combined to form a second reclassi cation representing repetitive production systems. To qualify as primarily having a nonrepetitive or repetitive production system, a manufacturer had to generate at least 70% of its sales from either of the reclassi ed production processes. Subsequently, the data collected on the middle production process (batch) were omitted from the sample for this study (N = 126). In addition, another 221 cases where a combination of processes used to generate sales were omitted from the nal sample. Throughout the rest of this study the term “production system” is used to refer to these production systems (nonrepetitive and=or repetitive). Ultimately, the eective sample for this study is 494 responses, of which 191 represent manufacturers with nonrepetitive production systems and 303 represent manufacturers with repetitive production systems.

5. Results and analysis A wide variety of industries was represented by the organizations included in the sample data. Seven categories of industries accounted for greater than 69% of those industries represented in the sample. The highest percent (39.0) of organizations represented in the sample was in the electronic=electric industry category. Other industries in the sample include metals, transportation equipment, machinery — except electric, medical components, rubber=plastics, and furniture represented by 9.1, 6.9, 4.3, 3.9, 3.9, and 2.4 percent of the organizations, respectively. Several other industries collectively accounted for 30.5% of the remaining manufacturers in the sample. 5.1. Implementation status of JIT practices Sample statistics for implementation status of speci c JIT practices by production system are presented in Table 2. The data indicate quality circles, total quality control and reduced setup times have the largest means for implementation status in repetitive production systems, with 2.47, 2.26, and 2.20, respectively. Total productive maintenance had the smallest mean (1.29) for implementation status in repetitive production systems. Multifunction employees and quality circles had the largest means for implementation status in nonrepetitive production systems, with 1.73 and 1.72, respectively. Uniform workload had the smallest mean (0.78) in nonrepetitive production systems.

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Table 2 Mean implementation status and number of production systems with JIT practices implemented JIT management practices

Production systema

Implementation status Mean SD

Number of production systems with practices Implemented=not implemented

Percent with practices implemented

Quality circles

Non Rep

1.72 2.47

2.12 2.29

113 223

78 80

59.2 73.6

Total quality control

Non Rep

1.69 2.26

1.62 1.88

161 268

30 35

84.3 88.4

Focused factory

Non Rep

1.20 1.83

1.51 1.76

122 228

69 75

63.9 75.2

Total productive maintenance

Non Rep

0.83 1.29

1.40 1.55

101 199

90 104

52.9 65.7

Reduced setup times

Non Rep

1.51 2.20

1.42 1.74

157 269

34 34

82.2 88.8

Group technology

Non Rep

1.35 1.74

1.66 1.80

121 213

70 90

63.3 70.3

Uniform workload

Non Rep

0.78 1.55

1.25 1.82

87 203

104 100

45.5 67.0

Multifunction employees

Non Rep

1.73 2.01

1.65 1.86

157 248

34 55

82.2 81.8

Kanban

Non Rep

0.96 1.92

1.31 1.85

115 220

76 83

60.2 72.6

Just-in-time purchasing

Non Rep

1.22 1.82

1.32 1.73

138 247

53 56

72.3 81.5

a Non

= nonrepetitive production system (N = 191), Rep = repetitive production system (N = 303).

5.2. Implemented JIT practices To provide data for testing association between implemented JIT practices and production system, and comparing results of this study with those of other survey studies [22– 24,38], the data for assessing implementation status were recoded with a score of 0 if the respondent checked “not implemented” and 1 if otherwise. This dichotomous coding scheme allows computation and comparison of the frequencies of implementations of the JIT practices across the 10 practices assessed in each production system. A summary of these results is presented in Table 2. Sample statistics for bene ts attributed to JIT implementation are presented in Table 3. Better throughput time was the most frequent bene t cited by the respondents in both nonrepetitive and repetitive production systems 86.9 and 89.1%, respectively. The second most frequent bene t cited by the respondents was better internal quality with 80.1 and 88.4% for nonrepetitive and repetitive systems, respectively. Lower unit cost was the bene t least frequently cited by both nonrepetitive and repetitive systems with 56.0 and 68.6%, respectively. The second least frequently cited bene t attributed to JIT was better external quality with 65.9 and 71.0% for

nonrepetitive and repetitive production systems, respectively. 5.3. Odds ratio The odds ratio of JIT practices (implemented vs. not implemented) were constructed to examine the association between implemented and production system for each JIT practice (Table 4). The odds ratio is de ned as the ratio of the odds for x = 1 (implemented) to the odds for x = 0 (not implemented). In other words, the odds ratio is a measure of association that approximates how likely (or unlikely) is an outcome (x = 1) versus the absence of that outcome (x = 0). A signi cant negative association (odds ratio ¡ 1:000) was indicated between nonrepetitive production systems and implemented for seven of the JIT practices (quality circles, focused factory, total productive maintenance, reduced setup times, uniform workload, Kanban, and JIT purchasing). The negative association for each of these practices suggests nonrepetitive production systems are less likely to implement JIT practices than repetitive production systems. No signi cant association between production systems and implemented was indicated for total quality control, group technology or multifunction employees. Thus, the evidence

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Table 3 Bene ts attributed to JIT implementation Bene ts

Production systema

Cases indicating Not better=better

Throughput time

Non Rep

25 33

166 270

86.9 89.1

Internal quality

Non Rep

38 35

153 268

80.1 88.4

External quality

Non Rep

65 88

126 215

65.9 71.0

Labor productivity

Non Rep

59 63

132 240

69.1 79.2

Inventory levels

Non Rep

Cases indicating Not lower=lower 43 38

148 265

77.5 87.5

Unit cost

Non Rep

84 95

107 208

56.0 68.6

a Non

Percent better

Percent lower

= nonrepetitive production system (N = 191), Rep = repetitive production system (N = 303).

Table 4 Odds ratio for JIT practices (implemented vs. not implemented) in nonrepetitive and repetitive production systems JIT practices

Production systema

Number of systems with practices

Odds ratio

95% con dence interval

Signi cance

Quality circles

Non Rep

113 223

78 80

0.520

(0.354, 0.764)

Yes

Total quality control

Non Rep

161 268

30 35

0.701

(0.415, 1.185)

No

Focused factory

Non Rep

122 228

69 75

0.582

(0.392, 0.863)

Yes

Total productive maintenance

Non Rep

101 199

90 104

0.587

(0.405, 0.849)

Yes

Reduced setup times

Non Rep

157 269

34 34

0.584

(0.349, 0.976)

Yes

Group technology

Non Rep

121 213

70 90

0.730

(0.498, 1.072)

No

Uniform workload

Non Rep

87 203

104 100

0.412

(0.284, 0.598)

Yes

Multifunction employees

Non Rep

157 248

34 55

1.024

(0.639, 1.642)

No

Kanban

Non Rep

115 220

76 83

0.571

(0.389, 0.838)

Yes

Just-in-time purchasing

Non Rep

138 247

53 56

0.590

(0.384, 0.907)

Yes

a Non

implemented=not implemented

= nonrepetitive production system (N = 191), Rep = repetitive production system (N = 303).

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Table 5 Fitted logistic regression models for bene ts associated with JIT practices in nonrepetitive production systemsa Independent variables

Response variables

Coecients (SE)

Better throughput time

Constant

0.948b (0.308)

Better internal quality

Better external quality

Better labor productivity

0.931b (0.275)

Lower inventory levels

Lower unit cost

0.830b (0.211)

QC 0.413b (0.130)

TQC 0.519c (0.202)

FF TPM

−0:523c (0.206)

SU GT

0.979b (0.356)

0.463b (0.116) 0.413c (0.189)

UW

0.215c (0.107)

MFE KAN

1.437c (0.581)

0.557b (0.212)

−0:420c (0.188)

JITP −2 log likelihood

0.722b (0.263)

116.576

169.801

221.897

231.723

193.947

241.576

a Implementation

Status: QC = quality circles, TQC = total quality control, FF = focused factory, TPM = total productive maintenance, SU = reduced setup times, GT = group technology, UW = uniform workload, MFE = multi-function employees, KAN = Kanban, and JITP = Just-in-time purchasing, N = 191. b p 6 0:01. c p 6 0:05.

supports rejection of the null hypothesis (H0 1) and acceptance of the alternative hypothesis that an association exists between the JIT practices implemented and type of production system. 5.4. Logistic regression models 5.4.1. Implementation status of JIT practices and beneÿts Logistic regression models are appropriate to examine how bene ts attributed to JIT implementation are aected by the implementation status of each of the 10 JIT management practices in repetitive and nonrepetitive production systems. In each logistic model, implementation status of each of the 10 JIT management practices (ordinal scale) represent the explanatory variables and the binary response variable is the bene t (six assessed in this study) attributed to JIT implementation. The regression coecients estimate the impact of the independent variables on the probability of

achieving a bene t (better throughput time, internal quality level, external quality level, labor productivity, and lower inventory levels, unit cost). Thus, there are six models for repetitive production systems and six models for nonrepetitive systems. Each of the models has 11 parameters. The results obtained from the t of these logistic regression models for nonrepetitive and repetitive production systems are summarized in Tables 5 and 6, respectively. The columns in each table contain the coecients for each logistic model for the implementation status of the indicated JIT management practice. A positive coecient indicates increasing the implementation status for that JIT practice tends to increase the probability of achieving the bene t. A negative coecient indicates the opposite eect. All nonsignificant parameter estimates (p ¿ 0:05) in the tted models were omitted from the reported tables. Overall, the results of the tted logistic models suggest that implementation status of speci c JIT management

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Table 6 Fitted logistic regression models for bene ts associated with JIT practices in repetitive production systemsa Independent variables

Response variables

Coecients (SE)

Better throughput time

Better internal quality

Better external quality

Better labor productivity

1.135b (0.223)

Constant

Lower inventory levels

Lower unit cost

0.785b (0.276)

QC 0.439b (0.097)

TQC

0.275b (0.100)

0.383b (0.142) 0.396b (0.100)

FF TPM SU GT UW MFE

0.582b (0.198)

KAN

0.899b (0.252)

0.221b (0.085)

0.292b (0.103)

0.788b (0.192)

0.323b (0.090) 0.376c (0.156)

JITP −2 log likelihood

158.938

187.374

321.125

283.768

204.389

327.429

a Implementation

status: QC = quality circles, TQC = total quality control, FF = focused factory, TPM = total productive maintenance, SU = reduced setup times, GT = group technology, UW = uniform workload, MFE = multi-function employees, KAN = Kanban, and JITP = just-in-time purchasing, N = 303. b p 6 0:01. c p 6 0:05.

practices and production system aect bene ts attributed to JIT implementation and dierences exist in the relationships between the explanatory variables and the response variable in the logistic models for nonrepetitive production systems compared to those for repetitive production systems. Moreover, the results indicate implementation status of three JIT practices, total quality control (TQC), multifunction employees (MFE), and Kanban (KAN), have similar relationships (signi cant and positive) in four of the logistic regression models (total quality control (TQC) and better external quality, multifunction employees (MFE) and better labor productivity, Kanban (KAN) and better throughput time, and Kanban (KAN) and better internal quality) in both nonrepetitive and repetitive production systems. The tted models suggest that bene ts are aected dierently by implementation status of the JIT practices in nonrepetitive and repetitive production systems with seven of the 10 practices. Therefore, the second hypothesis (H0 2) is rejected and the alternative hypothesis, the relative bene t

from each JIT practice is not the same regardless of type of production system, is supported. 6. Discussion The JIT manufacturing concept was de ned in this study as a system of management practices, any of which could be adopted in a US manufacturing environment and would contribute to bene ts for the organization. Overall, repetitive production systems appear to be more progressive in their utilization of JIT practices than nonrepetitive production systems. They have a higher utilization of each of the JIT practices compared to nonrepetitive systems. The results of the odds ratios indicate there is an association between the implemented JIT management practices and type of production system with seven of the practices investigated (quality circles, focused factory, total productive maintenance, reduced setup times, uniform workload, Kanban, and JIT

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purchasing). With each of the seven practices, the results suggest that nonrepetitive production systems are less likely to implement than repetitive production systems. This general bias for repetitive production systems was expected because JIT was designed in and had its roots in a repetitive production system, Toyota. In addition, early researchers promoted JIT applications in repetitive systems [1,48,49]. For example, Shingo [1], in his description of the Toyota Production System, suggested that Kanban was applicable to repetitive production systems. Im and Schonberger [48] suggest that Kanban is good practice to implement in a repetitive production process; it provides the pull system for linking the production activities and a level production schedule (uniform workload) gives a stable environment for introducing Kanban. In addition, initial implementations of JIT in the US started in repetitive production systems, e.g., the automotive industry. Greater than 50% utilization of each of the 10 JIT practices existed in both production systems, except for the uniform workload practice in nonrepetitive production systems. Only 45.5% of the organizations with nonrepetitive production systems utilized uniform workload whereas 67% in repetitive productive systems utilized uniform workload. In addition, a signi cant relationship existed between implementation of uniform workload and type of production system. Implementation of uniform workload in a nonrepetitive production system appears counter to the traditional approach of manufacturing in nonrepetitive production systems. However, it does not involve building high levels of raw materials and nished goods in order to develop eciencies in the internal operations, but initially involves implementing group technology (cellular layout) and reduced setup times. This allows a nonrepetitive manufacturer to level production schedules through responsiveness and adapting to uctuations by changing the product mix. It is the responsiveness of the production system that dictates the organization’s eectiveness for satisfying customers’ needs and adapting to disruption in supply, and minimizes the adverse eects from uctuations in customer requirements and disruption in supply on the organization’s internal operating eciency. In addition, leveling an organization’s production schedule involves smoothing demand uctuations by working with downstream activities (working more closely with the customers), and upstream activities (working more closely with suppliers). The nding that implementation of quality circles is more likely in a repetitive production system than in a nonrepetitive system was an unexpected result. Compared to employees in repetitive production systems, employees in nonrepetitive systems often are required to perform a variety of tasks and develop necessary skills that allow for eective utilization of labor. Division of labor and specialization of skills, traditionally viewed as opportunities to achieve higher labor eciency in repetitive productive systems, are contrary to what is expected of employees in quality circles

(problem solving skills, decision making, etc.). However, the decision to implement quality circles may have been a rational decision centered around value; this was not given support in the logistic regression models, as no signi cant relationships existed between quality circles and any of the response variables. Perhaps, the explanation is somewhere between the ndings of McLachlin [50] and those of Inman and Boothe [37]. In an empirical study, McLachlin [50] found that organizations do not achieve high levels of JIT

ow and high levels of JIT quality without obtaining a high level of employee involvement. Based on the ndings of their survey study, Inman and Boothe [37] suggest that quality circles provide a foundation for implementation of JIT. According to Inman and Boothe, “: : : the use of quality circles could possibly facilitate JIT implementation because a number of QC rms in the study realized better results on some elements of JIT implementation”. Perhaps, the managers of these production systems understand this issue associated with employee involvement. A second unexpected nding involved the multi-function employee practice. There was little dierence in the frequency of utilization exhibited with multi-function employees between the two production systems. The cross training of employees on dierent machines and in dierent functions (associated with multi-function employees) is contrary to the characteristics of repetitive production systems (i.e., low worker skill level, small worker job content); therefore, implementation of multi-function employees in repetitive production systems is not an easy task. Perhaps, implementation and applicability of multi-function employees may be viewed as infeasible by managers of repetitive production systems and, consequently, implemented relatively less frequently when compared to nonrepetitive production systems. A third unexpected nding involved the JIT practice of group technology. With this practice, no signi cant relationship existed between implementation and type of production system. Hall [49] suggests that group technology is a key step for implementation of JIT in job shops. According to Hall, group technology (cellular layout) allows a job shop to convert their operation to more of ow-oriented operation. This is the rst step in intensifying the interdependence between operations [44]. However, there are deterrents that job shops have for implementing group technology. For example, a substantial investment may be required for implementing group technology (equipment and facilities) in nonrepetitive production systems. In addition, Gargeya and Thompson [51] suggest that “: : : the very nature of job shops’ intermittent processes creates a resistance to the simpli ed Kanban production control system” (p. 26). With the deterrents that exist for implementing group technology and nding no signi cant dierence exists between implementation and type of production system, this may suggest the value of group technology as part of JIT implementations in nonrepetitive systems. Further support is provided by the results of logistic regression models. Group technology, in

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nonrepetitive production system models, was signi cantly related to better throughput time. No signi cant relationships existed between group technology and response variables in the repetitive production system models. Three JIT practices have a similar impact on the bene ts investigated in this study in the nonrepetitive and repetitive logistic regression models. Signi cant and positive relationships existed between total quality control and better external quality, multi-function employees and better labor productivity, and Kanban and better throughput time. These ndings suggest that the importance of these JIT practices for obtaining these bene ts remains relatively constant regardless of the type of production system. After reviewing the ndings of the related study by White et al. [16], it was discovered that the multi-function employees practice had a similar impact (signi cant and positive) on improving productivity, in nonrepetitive and repetitive production systems, and small and large manufacturers. Perhaps, this suggests that regardless of the organization, if the focus when implementing JIT is to improve productivity, it is important to cross train employees on dierent machines and dierent functions. Generalizations of the ndings of this study should be treated with some caution. First, the respondents were not randomly selected, but represented manufacturers that are considered leaders in implementing world-class management practices. Second, only those completed surveys for organizations that had implemented any of the JIT practices were included in the nal sample. However the percentage of practices implemented are not unlike those of other studies [22–24,38]. Third, the percentage of electronic=electric manufacturers was 39.0% in the nal sample. However, in an eort to address concerns about a potential bias that might be introduced by the large percentage of rms in the electronic=electric manufacturers category we ran all our analysis with only the electronic=electric manufacturers and then again with all other rms excluding the electronic=electric manufacturers. As a result we did nd a few variables that were signi cant in the model using all the data that were signi cant for only electronic=electric manufactures or alternatively only for the groups excluding the electronic=electric manufactures. We believe that such a nding suggests a sample size issue rather than differences between electronic=electric manufactures and the other manufactures because the direction of the eects were the same but for some variables the signi cance was only apparent with the larger combined groups. The traditional view of managing US production systems is dicult to overcome. Understanding the JIT manufacturing concept and philosophy, the role of each practice, their interactions, and their organization is essential for eective implementation of JIT manufacturing. This study has shown that JIT manufacturing is utilized by both repetitive and nonrepetitive production systems. The results of the study suggest there is a dierence between nonrepetitive and repetitive production systems in their implementation of

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JIT practices. It is important to note that 7 of the 10 JIT practices produced signi cant dierences in implemented practices for nonrepetitive compared to repetitive production systems. This does lend support to the premise that much of JIT manufacturing is adaptable and applicable across dierent production systems.

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